Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)3.2%
Total size in memory8.0 KiB
Average record size in memory264.0 B

Variable types

Numeric11
Categorical21

Alerts

421, centrum.sk: count(campaign) has constant value "0"Constant
421, icloud.com: count(campaign) has constant value "0"Constant
499, azet.sk: count(campaign) has constant value "0"Constant
550, icloud.com: count(campaign) has constant value "0"Constant
Dataset has 1 (3.2%) duplicate rowsDuplicates
421, (other): count(campaign) is highly overall correlated with 421, azet.sk: count(campaign) and 4 other fieldsHigh correlation
421, azet.sk: count(campaign) is highly overall correlated with 421, (other): count(campaign) and 5 other fieldsHigh correlation
421, seznam.cz: count(campaign) is highly overall correlated with 421, (other): count(campaign) and 9 other fieldsHigh correlation
421, zoznam.sk: count(campaign) is highly overall correlated with 421, (other): count(campaign) and 12 other fieldsHigh correlation
451, (other): count(campaign) is highly overall correlated with 421, seznam.cz: count(campaign) and 8 other fieldsHigh correlation
451, centrum.sk: count(campaign) is highly overall correlated with 421, seznam.cz: count(campaign) and 11 other fieldsHigh correlation
451, hotmail.com: count(campaign) is highly overall correlated with 451, centrum.sk: count(campaign) and 1 other fieldsHigh correlation
451, icloud.com: count(campaign) is highly overall correlated with 451, (other): count(campaign) and 4 other fieldsHigh correlation
451, zoznam.sk: count(campaign) is highly overall correlated with 421, zoznam.sk: count(campaign) and 6 other fieldsHigh correlation
452, (other): count(campaign) is highly overall correlated with 421, zoznam.sk: count(campaign) and 7 other fieldsHigh correlation
452, gmail.com: count(campaign) is highly overall correlated with 421, seznam.cz: count(campaign) and 9 other fieldsHigh correlation
499, (other): count(campaign) is highly overall correlated with 421, seznam.cz: count(campaign) and 8 other fieldsHigh correlation
499, centrum.sk: count(campaign) is highly overall correlated with 421, azet.sk: count(campaign) and 9 other fieldsHigh correlation
499, seznam.cz: count(campaign) is highly overall correlated with 451, centrum.sk: count(campaign) and 4 other fieldsHigh correlation
499, zoznam.sk: count(campaign) is highly overall correlated with 421, (other): count(campaign) and 4 other fieldsHigh correlation
550, (other): count(campaign) is highly overall correlated with 550, gmail.com: count(campaign) and 6 other fieldsHigh correlation
550, gmail.com: count(campaign) is highly overall correlated with 550, (other): count(campaign) and 5 other fieldsHigh correlation
550, hotmail.com: count(campaign) is highly overall correlated with 550, seznam.cz: count(campaign) and 5 other fieldsHigh correlation
550, seznam.cz: count(campaign) is highly overall correlated with 421, (other): count(campaign) and 3 other fieldsHigh correlation
550, zoznam.sk: count(campaign) is highly overall correlated with 451, centrum.sk: count(campaign) and 7 other fieldsHigh correlation
552, (other): count(campaign) is highly overall correlated with 552, gmail.com: count(campaign)High correlation
552, gmail.com: count(campaign) is highly overall correlated with 550, (other): count(campaign) and 8 other fieldsHigh correlation
552, seznam.cz: count(campaign) is highly overall correlated with 421, zoznam.sk: count(campaign) and 1 other fieldsHigh correlation
554, (other): count(campaign) is highly overall correlated with 421, seznam.cz: count(campaign) and 6 other fieldsHigh correlation
554, azet.sk: count(campaign) is highly overall correlated with 421, azet.sk: count(campaign) and 1 other fieldsHigh correlation
554, centrum.sk: count(campaign) is highly overall correlated with 421, seznam.cz: count(campaign) and 3 other fieldsHigh correlation
554, stonline.sk: count(campaign) is highly overall correlated with 499, zoznam.sk: count(campaign) and 6 other fieldsHigh correlation
554, yahoo.com: count(campaign) is highly overall correlated with 499, seznam.cz: count(campaign) and 6 other fieldsHigh correlation
421, seznam.cz: count(campaign) is highly imbalanced (79.4%)Imbalance
421, zoznam.sk: count(campaign) is highly imbalanced (65.0%)Imbalance
421, (other): count(campaign) is highly imbalanced (65.5%)Imbalance
451, centrum.sk: count(campaign) is highly imbalanced (69.4%)Imbalance
451, hotmail.com: count(campaign) is highly imbalanced (74.2%)Imbalance
451, zoznam.sk: count(campaign) is highly imbalanced (79.4%)Imbalance
499, centrum.sk: count(campaign) is highly imbalanced (65.5%)Imbalance
499, seznam.cz: count(campaign) is highly imbalanced (79.4%)Imbalance
499, zoznam.sk: count(campaign) is highly imbalanced (79.4%)Imbalance
550, hotmail.com: count(campaign) is highly imbalanced (79.4%)Imbalance
550, seznam.cz: count(campaign) is highly imbalanced (58.4%)Imbalance
550, zoznam.sk: count(campaign) is highly imbalanced (69.4%)Imbalance
554, azet.sk: count(campaign) is highly imbalanced (74.2%)Imbalance
554, stonline.sk: count(campaign) is highly imbalanced (65.5%)Imbalance
554, yahoo.com: count(campaign) is highly imbalanced (65.5%)Imbalance
421, azet.sk: count(campaign) has 20 (64.5%) zerosZeros
451, icloud.com: count(campaign) has 14 (45.2%) zerosZeros
451, (other): count(campaign) has 13 (41.9%) zerosZeros
452, gmail.com: count(campaign) has 13 (41.9%) zerosZeros
452, (other): count(campaign) has 15 (48.4%) zerosZeros
499, (other): count(campaign) has 13 (41.9%) zerosZeros
550, gmail.com: count(campaign) has 14 (45.2%) zerosZeros
550, (other): count(campaign) has 13 (41.9%) zerosZeros
552, gmail.com: count(campaign) has 12 (38.7%) zerosZeros
554, centrum.sk: count(campaign) has 17 (54.8%) zerosZeros
554, (other): count(campaign) has 12 (38.7%) zerosZeros

Reproduction

Analysis started2024-10-02 21:20:16.851243
Analysis finished2024-10-02 21:20:37.694099
Duration20.84 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

421, azet.sk: count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5806452
Minimum0
Maximum44
Zeros20
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:37.817661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile29
Maximum44
Range44
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation11.002346
Coefficient of variation (CV)2.4019206
Kurtosis6.1022693
Mean4.5806452
Median Absolute Deviation (MAD)0
Skewness2.620488
Sum142
Variance121.05161
MonotonicityNot monotonic
2024-10-03T02:50:37.954875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 20
64.5%
1 3
 
9.7%
2 2
 
6.5%
3 1
 
3.2%
25 1
 
3.2%
44 1
 
3.2%
33 1
 
3.2%
6 1
 
3.2%
24 1
 
3.2%
ValueCountFrequency (%)
0 20
64.5%
1 3
 
9.7%
2 2
 
6.5%
3 1
 
3.2%
6 1
 
3.2%
24 1
 
3.2%
25 1
 
3.2%
33 1
 
3.2%
44 1
 
3.2%
ValueCountFrequency (%)
44 1
 
3.2%
33 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
6 1
 
3.2%
3 1
 
3.2%
2 2
 
6.5%
1 3
 
9.7%
0 20
64.5%

421, centrum.sk: count(campaign)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2024-10-03T02:50:38.130769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:38.285291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
0 31
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
100.0%

421, icloud.com: count(campaign)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2024-10-03T02:50:38.419689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:38.582665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
0 31
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
100.0%

421, seznam.cz: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
30 
19
 
1

Length

Max length2
Median length1
Mean length1.0322581
Min length1

Characters and Unicode

Total characters32
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
19 1
 
3.2%

Length

2024-10-03T02:50:38.716778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:38.875317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
19 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 30
93.8%
1 1
 
3.1%
9 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
93.8%
1 1
 
3.1%
9 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
93.8%
1 1
 
3.1%
9 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
93.8%
1 1
 
3.1%
9 1
 
3.1%

421, zoznam.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
27 
1
 
1
15
 
1
6
 
1
16
 
1

Length

Max length2
Median length1
Mean length1.0645161
Min length1

Characters and Unicode

Total characters33
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)12.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27
87.1%
1 1
 
3.2%
15 1
 
3.2%
6 1
 
3.2%
16 1
 
3.2%

Length

2024-10-03T02:50:39.049183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:39.204902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
87.1%
1 1
 
3.2%
15 1
 
3.2%
6 1
 
3.2%
16 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 27
81.8%
1 3
 
9.1%
6 2
 
6.1%
5 1
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27
81.8%
1 3
 
9.1%
6 2
 
6.1%
5 1
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27
81.8%
1 3
 
9.1%
6 2
 
6.1%
5 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27
81.8%
1 3
 
9.1%
6 2
 
6.1%
5 1
 
3.0%

421, (other): count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
28 
2
 
2
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
90.3%
2 2
 
6.5%
1 1
 
3.2%

Length

2024-10-03T02:50:39.377249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:39.507326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
90.3%
2 2
 
6.5%
1 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 28
90.3%
2 2
 
6.5%
1 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
90.3%
2 2
 
6.5%
1 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
90.3%
2 2
 
6.5%
1 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
90.3%
2 2
 
6.5%
1 1
 
3.2%

451, centrum.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
28 
2
 
1
1
 
1
9
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)9.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
90.3%
2 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%

Length

2024-10-03T02:50:39.681033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:39.840785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
90.3%
2 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 28
90.3%
2 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
90.3%
2 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
90.3%
2 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
90.3%
2 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%

451, hotmail.com: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
29 
1
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)6.5%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
93.5%
1 1
 
3.2%
2 1
 
3.2%

Length

2024-10-03T02:50:39.993422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:40.168657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
93.5%
1 1
 
3.2%
2 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 29
93.5%
1 1
 
3.2%
2 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29
93.5%
1 1
 
3.2%
2 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29
93.5%
1 1
 
3.2%
2 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
93.5%
1 1
 
3.2%
2 1
 
3.2%

451, icloud.com: count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6774194
Minimum0
Maximum12
Zeros14
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:40.296241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile11
Maximum12
Range12
Interquartile range (IQR)10

Descriptive statistics

Standard deviation4.8674915
Coefficient of variation (CV)1.0406361
Kurtosis-1.9531278
Mean4.6774194
Median Absolute Deviation (MAD)1
Skewness0.16526791
Sum145
Variance23.692473
MonotonicityNot monotonic
2024-10-03T02:50:40.460028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14
45.2%
10 6
19.4%
8 5
 
16.1%
11 2
 
6.5%
1 2
 
6.5%
12 1
 
3.2%
9 1
 
3.2%
ValueCountFrequency (%)
0 14
45.2%
1 2
 
6.5%
8 5
 
16.1%
9 1
 
3.2%
10 6
19.4%
11 2
 
6.5%
12 1
 
3.2%
ValueCountFrequency (%)
12 1
 
3.2%
11 2
 
6.5%
10 6
19.4%
9 1
 
3.2%
8 5
 
16.1%
1 2
 
6.5%
0 14
45.2%

451, zoznam.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
30 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
4 1
 
3.2%

Length

2024-10-03T02:50:40.625797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:40.761520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
4 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 30
96.8%
4 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
96.8%
4 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
96.8%
4 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
96.8%
4 1
 
3.2%

451, (other): count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.483871
Minimum0
Maximum10
Zeros13
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:40.884017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3075379
Coefficient of variation (CV)1.5550799
Kurtosis6.9906795
Mean1.483871
Median Absolute Deviation (MAD)1
Skewness2.545041
Sum46
Variance5.3247312
MonotonicityNot monotonic
2024-10-03T02:50:41.027601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 13
41.9%
1 9
29.0%
3 3
 
9.7%
2 3
 
9.7%
4 1
 
3.2%
8 1
 
3.2%
10 1
 
3.2%
ValueCountFrequency (%)
0 13
41.9%
1 9
29.0%
2 3
 
9.7%
3 3
 
9.7%
4 1
 
3.2%
8 1
 
3.2%
10 1
 
3.2%
ValueCountFrequency (%)
10 1
 
3.2%
8 1
 
3.2%
4 1
 
3.2%
3 3
 
9.7%
2 3
 
9.7%
1 9
29.0%
0 13
41.9%

452, gmail.com: count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.064516
Minimum0
Maximum60
Zeros13
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:41.167713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q342.5
95-th percentile52.5
Maximum60
Range60
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation23.359417
Coefficient of variation (CV)1.058687
Kurtosis-1.8786703
Mean22.064516
Median Absolute Deviation (MAD)2
Skewness0.20875984
Sum684
Variance545.66237
MonotonicityNot monotonic
2024-10-03T02:50:41.324852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 13
41.9%
52 3
 
9.7%
2 2
 
6.5%
39 2
 
6.5%
41 2
 
6.5%
43 2
 
6.5%
1 1
 
3.2%
42 1
 
3.2%
37 1
 
3.2%
36 1
 
3.2%
Other values (3) 3
 
9.7%
ValueCountFrequency (%)
0 13
41.9%
1 1
 
3.2%
2 2
 
6.5%
36 1
 
3.2%
37 1
 
3.2%
39 2
 
6.5%
41 2
 
6.5%
42 1
 
3.2%
43 2
 
6.5%
49 1
 
3.2%
ValueCountFrequency (%)
60 1
 
3.2%
53 1
 
3.2%
52 3
9.7%
49 1
 
3.2%
43 2
6.5%
42 1
 
3.2%
41 2
6.5%
39 2
6.5%
37 1
 
3.2%
36 1
 
3.2%

452, (other): count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1935484
Minimum0
Maximum8
Zeros15
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:41.486004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7013594
Coefficient of variation (CV)1.4254632
Kurtosis7.6734271
Mean1.1935484
Median Absolute Deviation (MAD)1
Skewness2.3684831
Sum37
Variance2.8946237
MonotonicityNot monotonic
2024-10-03T02:50:41.637464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 15
48.4%
2 7
22.6%
1 5
 
16.1%
3 2
 
6.5%
4 1
 
3.2%
8 1
 
3.2%
ValueCountFrequency (%)
0 15
48.4%
1 5
 
16.1%
2 7
22.6%
3 2
 
6.5%
4 1
 
3.2%
8 1
 
3.2%
ValueCountFrequency (%)
8 1
 
3.2%
4 1
 
3.2%
3 2
 
6.5%
2 7
22.6%
1 5
 
16.1%
0 15
48.4%

499, azet.sk: count(campaign)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2024-10-03T02:50:41.810961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:41.934807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
0 31
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
100.0%

499, centrum.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
28 
1
 
2
14
 
1

Length

Max length2
Median length1
Mean length1.0322581
Min length1

Characters and Unicode

Total characters32
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
90.3%
1 2
 
6.5%
14 1
 
3.2%

Length

2024-10-03T02:50:42.079952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:42.395872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
90.3%
1 2
 
6.5%
14 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

499, seznam.cz: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
30 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Length

2024-10-03T02:50:42.538835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:42.675946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

499, zoznam.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
30 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
2 1
 
3.2%

Length

2024-10-03T02:50:42.854156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:42.989681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
2 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 30
96.8%
2 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
96.8%
2 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
96.8%
2 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
96.8%
2 1
 
3.2%

499, (other): count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9677419
Minimum0
Maximum47
Zeros13
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:43.122551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314.5
95-th percentile20
Maximum47
Range47
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation10.335969
Coefficient of variation (CV)1.2972269
Kurtosis5.4165213
Mean7.9677419
Median Absolute Deviation (MAD)1
Skewness1.8878529
Sum247
Variance106.83226
MonotonicityNot monotonic
2024-10-03T02:50:43.279811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 13
41.9%
1 3
 
9.7%
16 3
 
9.7%
12 2
 
6.5%
13 2
 
6.5%
15 2
 
6.5%
8 1
 
3.2%
7 1
 
3.2%
22 1
 
3.2%
14 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
0 13
41.9%
1 3
 
9.7%
7 1
 
3.2%
8 1
 
3.2%
12 2
 
6.5%
13 2
 
6.5%
14 1
 
3.2%
15 2
 
6.5%
16 3
 
9.7%
18 1
 
3.2%
ValueCountFrequency (%)
47 1
 
3.2%
22 1
 
3.2%
18 1
 
3.2%
16 3
9.7%
15 2
6.5%
14 1
 
3.2%
13 2
6.5%
12 2
6.5%
8 1
 
3.2%
7 1
 
3.2%

550, gmail.com: count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7096774
Minimum0
Maximum137
Zeros14
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:43.425919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum137
Range137
Interquartile range (IQR)2

Descriptive statistics

Standard deviation24.418836
Coefficient of variation (CV)4.2767454
Kurtosis30.707222
Mean5.7096774
Median Absolute Deviation (MAD)1
Skewness5.5298791
Sum177
Variance596.27957
MonotonicityNot monotonic
2024-10-03T02:50:43.565267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14
45.2%
2 6
19.4%
1 4
 
12.9%
4 3
 
9.7%
3 2
 
6.5%
6 1
 
3.2%
137 1
 
3.2%
ValueCountFrequency (%)
0 14
45.2%
1 4
 
12.9%
2 6
19.4%
3 2
 
6.5%
4 3
 
9.7%
6 1
 
3.2%
137 1
 
3.2%
ValueCountFrequency (%)
137 1
 
3.2%
6 1
 
3.2%
4 3
 
9.7%
3 2
 
6.5%
2 6
19.4%
1 4
 
12.9%
0 14
45.2%

550, hotmail.com: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
30 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
5 1
 
3.2%

Length

2024-10-03T02:50:43.735210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:43.872651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
5 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 30
96.8%
5 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
96.8%
5 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
96.8%
5 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
96.8%
5 1
 
3.2%

550, icloud.com: count(campaign)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2024-10-03T02:50:44.007037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:44.144904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
0 31
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
100.0%

550, seznam.cz: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
27 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27
87.1%
1 3
 
9.7%
3 1
 
3.2%

Length

2024-10-03T02:50:44.275268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:44.426807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
87.1%
1 3
 
9.7%
3 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 27
87.1%
1 3
 
9.7%
3 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27
87.1%
1 3
 
9.7%
3 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27
87.1%
1 3
 
9.7%
3 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27
87.1%
1 3
 
9.7%
3 1
 
3.2%

550, zoznam.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
28 
16
 
1
6
 
1
1
 
1

Length

Max length2
Median length1
Mean length1.0322581
Min length1

Characters and Unicode

Total characters32
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)9.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
90.3%
16 1
 
3.2%
6 1
 
3.2%
1 1
 
3.2%

Length

2024-10-03T02:50:44.575253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:44.716711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
90.3%
16 1
 
3.2%
6 1
 
3.2%
1 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
6 2
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
6 2
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
6 2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
6 2
 
6.2%

550, (other): count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.709677
Minimum0
Maximum324
Zeros13
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:44.852654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36.5
95-th percentile12.5
Maximum324
Range324
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation57.724168
Coefficient of variation (CV)4.2104687
Kurtosis30.676871
Mean13.709677
Median Absolute Deviation (MAD)2
Skewness5.5259059
Sum425
Variance3332.0796
MonotonicityNot monotonic
2024-10-03T02:50:45.004956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 13
41.9%
4 3
 
9.7%
2 3
 
9.7%
8 2
 
6.5%
6 2
 
6.5%
12 1
 
3.2%
10 1
 
3.2%
3 1
 
3.2%
9 1
 
3.2%
324 1
 
3.2%
Other values (3) 3
 
9.7%
ValueCountFrequency (%)
0 13
41.9%
1 1
 
3.2%
2 3
 
9.7%
3 1
 
3.2%
4 3
 
9.7%
6 2
 
6.5%
7 1
 
3.2%
8 2
 
6.5%
9 1
 
3.2%
10 1
 
3.2%
ValueCountFrequency (%)
324 1
 
3.2%
13 1
 
3.2%
12 1
 
3.2%
10 1
 
3.2%
9 1
 
3.2%
8 2
6.5%
7 1
 
3.2%
6 2
6.5%
4 3
9.7%
3 1
 
3.2%

552, gmail.com: count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.419355
Minimum0
Maximum31
Zeros12
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:45.154870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q328
95-th percentile30.5
Maximum31
Range31
Interquartile range (IQR)28

Descriptive statistics

Standard deviation14.165155
Coefficient of variation (CV)1.0555764
Kurtosis-2.0514668
Mean13.419355
Median Absolute Deviation (MAD)2
Skewness0.18388295
Sum416
Variance200.65161
MonotonicityNot monotonic
2024-10-03T02:50:45.302101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12
38.7%
28 5
16.1%
27 3
 
9.7%
1 2
 
6.5%
29 2
 
6.5%
30 2
 
6.5%
2 2
 
6.5%
31 2
 
6.5%
9 1
 
3.2%
ValueCountFrequency (%)
0 12
38.7%
1 2
 
6.5%
2 2
 
6.5%
9 1
 
3.2%
27 3
 
9.7%
28 5
16.1%
29 2
 
6.5%
30 2
 
6.5%
31 2
 
6.5%
ValueCountFrequency (%)
31 2
 
6.5%
30 2
 
6.5%
29 2
 
6.5%
28 5
16.1%
27 3
 
9.7%
9 1
 
3.2%
2 2
 
6.5%
1 2
 
6.5%
0 12
38.7%

552, seznam.cz: count(campaign)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
27 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27
87.1%
1 4
 
12.9%

Length

2024-10-03T02:50:45.469467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:45.601345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
87.1%
1 4
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 27
87.1%
1 4
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27
87.1%
1 4
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27
87.1%
1 4
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27
87.1%
1 4
 
12.9%

552, (other): count(campaign)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
16 
2
1
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16
51.6%
2 6
 
19.4%
1 6
 
19.4%
4 3
 
9.7%

Length

2024-10-03T02:50:45.744243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:45.888181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16
51.6%
2 6
 
19.4%
1 6
 
19.4%
4 3
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 16
51.6%
2 6
 
19.4%
1 6
 
19.4%
4 3
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16
51.6%
2 6
 
19.4%
1 6
 
19.4%
4 3
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16
51.6%
2 6
 
19.4%
1 6
 
19.4%
4 3
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16
51.6%
2 6
 
19.4%
1 6
 
19.4%
4 3
 
9.7%

554, azet.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
29 
7
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)6.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
93.5%
7 1
 
3.2%
3 1
 
3.2%

Length

2024-10-03T02:50:46.047911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:46.194481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
93.5%
7 1
 
3.2%
3 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 29
93.5%
7 1
 
3.2%
3 1
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29
93.5%
7 1
 
3.2%
3 1
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 31
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29
93.5%
7 1
 
3.2%
3 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
93.5%
7 1
 
3.2%
3 1
 
3.2%

554, centrum.sk: count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.774194
Minimum0
Maximum564
Zeros17
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:46.310780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q346
95-th percentile344
Maximum564
Range564
Interquartile range (IQR)46

Descriptive statistics

Standard deviation131.76866
Coefficient of variation (CV)2.168168
Kurtosis7.5036274
Mean60.774194
Median Absolute Deviation (MAD)0
Skewness2.7224001
Sum1884
Variance17362.981
MonotonicityNot monotonic
2024-10-03T02:50:46.447945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 17
54.8%
1 2
 
6.5%
11 2
 
6.5%
26 1
 
3.2%
397 1
 
3.2%
291 1
 
3.2%
66 1
 
3.2%
143 1
 
3.2%
78 1
 
3.2%
158 1
 
3.2%
Other values (3) 3
 
9.7%
ValueCountFrequency (%)
0 17
54.8%
1 2
 
6.5%
2 1
 
3.2%
11 2
 
6.5%
26 1
 
3.2%
66 1
 
3.2%
78 1
 
3.2%
135 1
 
3.2%
143 1
 
3.2%
158 1
 
3.2%
ValueCountFrequency (%)
564 1
3.2%
397 1
3.2%
291 1
3.2%
158 1
3.2%
143 1
3.2%
135 1
3.2%
78 1
3.2%
66 1
3.2%
26 1
3.2%
11 2
6.5%

554, stonline.sk: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
28 
1
 
2
14
 
1

Length

Max length2
Median length1
Mean length1.0322581
Min length1

Characters and Unicode

Total characters32
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
90.3%
1 2
 
6.5%
14 1
 
3.2%

Length

2024-10-03T02:50:46.612694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:46.750458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
90.3%
1 2
 
6.5%
14 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
87.5%
1 3
 
9.4%
4 1
 
3.1%

554, yahoo.com: count(campaign)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
28 
1
 
2
39
 
1

Length

Max length2
Median length1
Mean length1.0322581
Min length1

Characters and Unicode

Total characters32
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28
90.3%
1 2
 
6.5%
39 1
 
3.2%

Length

2024-10-03T02:50:46.894145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-03T02:50:47.035253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
90.3%
1 2
 
6.5%
39 1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
3 1
 
3.1%
9 1
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
3 1
 
3.1%
9 1
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
3 1
 
3.1%
9 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28
87.5%
1 2
 
6.2%
3 1
 
3.1%
9 1
 
3.1%

554, (other): count(campaign)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.516129
Minimum0
Maximum332
Zeros12
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size496.0 B
2024-10-03T02:50:47.174022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q315
95-th percentile152
Maximum332
Range332
Interquartile range (IQR)15

Descriptive statistics

Standard deviation69.157246
Coefficient of variation (CV)2.4251976
Kurtosis12.720822
Mean28.516129
Median Absolute Deviation (MAD)2
Skewness3.4137588
Sum884
Variance4782.7247
MonotonicityNot monotonic
2024-10-03T02:50:47.326165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
38.7%
1 3
 
9.7%
2 3
 
9.7%
15 2
 
6.5%
166 1
 
3.2%
3 1
 
3.2%
13 1
 
3.2%
93 1
 
3.2%
11 1
 
3.2%
31 1
 
3.2%
Other values (5) 5
16.1%
ValueCountFrequency (%)
0 12
38.7%
1 3
 
9.7%
2 3
 
9.7%
3 1
 
3.2%
4 1
 
3.2%
11 1
 
3.2%
13 1
 
3.2%
15 2
 
6.5%
24 1
 
3.2%
30 1
 
3.2%
ValueCountFrequency (%)
332 1
3.2%
166 1
3.2%
138 1
3.2%
93 1
3.2%
31 1
3.2%
30 1
3.2%
24 1
3.2%
15 2
6.5%
13 1
3.2%
11 1
3.2%

Interactions

2024-10-03T02:50:35.007388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:19.530344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.011183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.514884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.017807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.526161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.262675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.707476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.238888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.834266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.331301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.121559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:19.679036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.123363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.621919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.134415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.669181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.392086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.834901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.367541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.957325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.459624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.267884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:19.805319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.264999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.763186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.292127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.794055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.517400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.977062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.511535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.095908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.587874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.402226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:19.957140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.412827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.879945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.422451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.934937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.647048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.096920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.635916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.219671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.731481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.548872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.074139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.545202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.066880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.561560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:26.087975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.773174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.247050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.781919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.373173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.864886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.712380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.224194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.700179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.201959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.694144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:26.232482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.914868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.377074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.921160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.504850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:34.016510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.832000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.361054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.814240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.341377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.828038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:26.382140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.026678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.521955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.057892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.641092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:34.323990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:35.985284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.486730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:21.964897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.493058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:24.957302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:26.701718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.179357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.662763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.193071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.778747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:34.466645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:36.105597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.635055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.092928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.617328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.110037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:26.832560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.307389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.821649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.339313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:32.929314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:34.588331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:36.246721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.746172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.242500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.761053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.240988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:26.988091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.437702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:29.967639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.487338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.047390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:34.739403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:36.379102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:20.885609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:22.367640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:23.878568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:25.385701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:27.135615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:28.564491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:30.094988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:31.674913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:33.196938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-03T02:50:34.865786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-03T02:50:47.592114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
421, (other): count(campaign)421, azet.sk: count(campaign)421, seznam.cz: count(campaign)421, zoznam.sk: count(campaign)451, (other): count(campaign)451, centrum.sk: count(campaign)451, hotmail.com: count(campaign)451, icloud.com: count(campaign)451, zoznam.sk: count(campaign)452, (other): count(campaign)452, gmail.com: count(campaign)499, (other): count(campaign)499, centrum.sk: count(campaign)499, seznam.cz: count(campaign)499, zoznam.sk: count(campaign)550, (other): count(campaign)550, gmail.com: count(campaign)550, hotmail.com: count(campaign)550, seznam.cz: count(campaign)550, zoznam.sk: count(campaign)552, (other): count(campaign)552, gmail.com: count(campaign)552, seznam.cz: count(campaign)554, (other): count(campaign)554, azet.sk: count(campaign)554, centrum.sk: count(campaign)554, stonline.sk: count(campaign)554, yahoo.com: count(campaign)
421, (other): count(campaign)1.0000.7260.6570.6280.4990.3920.0000.3490.0000.2440.4480.4580.4350.0000.6570.0000.0000.0000.6840.0000.0000.0000.1470.3940.0000.2990.2150.000
421, azet.sk: count(campaign)0.7261.0000.9470.7500.4680.4760.0000.3980.0000.4520.3840.4910.6310.0000.6020.138-0.0920.0000.3520.0000.1680.2160.4470.2720.6290.3480.0000.000
421, seznam.cz: count(campaign)0.6570.9471.0000.9470.9280.9650.0000.2030.0000.0000.9280.9280.9830.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.9650.0000.9280.0000.000
421, zoznam.sk: count(campaign)0.6280.7500.9471.0000.6260.7700.0000.2140.9470.5620.6980.6830.6300.0000.9470.0000.0000.0000.0000.4750.0000.0980.5890.4760.6280.3250.3450.000
451, (other): count(campaign)0.4990.4680.9280.6261.0000.8190.0000.8440.9280.7890.7690.7550.6540.3870.000-0.129-0.3820.0000.0920.4410.000-0.2020.0000.1590.0000.1470.0000.000
451, centrum.sk: count(campaign)0.3920.4760.9650.7700.8191.0000.6560.2840.9650.5220.8570.7870.8290.9650.0000.0000.0000.0000.0000.5110.0000.0000.0000.5120.0000.4400.0000.392
451, hotmail.com: count(campaign)0.0000.0000.0000.0000.0000.6561.0000.1990.0000.0000.3100.0000.4340.9830.0000.0000.0000.0000.3110.0000.2740.2740.0000.0000.0000.0000.0000.434
451, icloud.com: count(campaign)0.3490.3980.2030.2140.8440.2840.1991.0000.0760.8420.7880.7470.1560.4290.203-0.252-0.2570.0000.0000.0000.157-0.2740.5120.0940.0000.1010.0000.000
451, zoznam.sk: count(campaign)0.0000.0000.0000.9470.9280.9650.0000.0761.0000.9280.9280.9280.0000.0000.0000.0000.0000.0000.0000.9650.0000.0000.0000.0000.0000.0000.0000.000
452, (other): count(campaign)0.2440.4520.0000.5620.7890.5220.0000.8420.9281.0000.7690.8400.0000.0000.572-0.316-0.4730.0000.0000.4600.000-0.4170.283-0.0260.000-0.0340.0000.000
452, gmail.com: count(campaign)0.4480.3840.9280.6980.7690.8570.3100.7880.9280.7691.0000.8710.6930.5720.083-0.246-0.3300.0000.0000.4560.000-0.1770.4770.1080.0000.1400.0000.000
499, (other): count(campaign)0.4580.4910.9280.6830.7550.7870.0000.7470.9280.8400.8711.0000.6280.0830.000-0.075-0.3310.0000.0000.4560.000-0.2040.0000.1500.0000.1630.0000.000
499, centrum.sk: count(campaign)0.4350.6310.9830.6300.6540.8290.4340.1560.0000.0000.6930.6281.0000.6570.0000.0000.0000.0000.0000.0000.0000.0000.0000.6580.0000.6070.0000.215
499, seznam.cz: count(campaign)0.0000.0000.0000.0000.3870.9650.9830.4290.0000.0000.5720.0830.6571.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.657
499, zoznam.sk: count(campaign)0.6570.6020.0000.9470.0000.0000.0000.2030.0000.5720.0830.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.4670.0880.0000.0000.0000.6570.000
550, (other): count(campaign)0.0000.1380.0000.000-0.1290.0000.000-0.2520.000-0.316-0.246-0.0750.0000.0000.0001.0000.5880.4550.5030.9650.4670.6910.0000.6330.0000.4480.9830.983
550, gmail.com: count(campaign)0.000-0.0920.0000.000-0.3820.0000.000-0.2570.000-0.473-0.330-0.3310.0000.0000.0000.5881.0000.4550.5030.9650.4670.5640.0000.3840.0000.3170.9830.983
550, hotmail.com: count(campaign)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4550.4551.0000.5030.9650.4670.9650.0000.6300.0000.3870.9830.983
550, seznam.cz: count(campaign)0.6840.3520.0000.0000.0920.0000.3110.0000.0000.0000.0000.0000.0000.0000.0000.5030.5030.5031.0000.4890.4590.3880.0000.0000.0000.2030.3140.314
550, zoznam.sk: count(campaign)0.0000.0000.0000.4750.4410.5110.0000.0000.9650.4600.4560.4560.0000.0000.0000.9650.9650.9650.4891.0000.2550.5400.3680.2650.0000.2370.6570.657
552, (other): count(campaign)0.0000.1680.2010.0000.0000.0000.2740.1570.0000.0000.0000.0000.0000.0000.0000.4670.4670.4670.4590.2551.0000.5770.0960.2570.2160.3030.2940.383
552, gmail.com: count(campaign)0.0000.2160.0000.098-0.2020.0000.274-0.2740.000-0.417-0.177-0.2040.0000.0000.4670.6910.5640.9650.3880.5400.5771.0000.4970.5690.0000.5120.7160.659
552, seznam.cz: count(campaign)0.1470.4470.0000.5890.0000.0000.0000.5120.0000.2830.4770.0000.0000.0000.0880.0000.0000.0000.0000.3680.0960.4971.0000.0000.4080.3490.1470.000
554, (other): count(campaign)0.3940.2720.9650.4760.1590.5120.0000.0940.000-0.0260.1080.1500.6580.0000.0000.6330.3840.6300.0000.2650.2570.5690.0001.0000.0000.8690.3940.394
554, azet.sk: count(campaign)0.0000.6290.0000.6280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2160.0000.4080.0001.0000.2930.0000.000
554, centrum.sk: count(campaign)0.2990.3480.9280.3250.1470.4400.0000.1010.000-0.0340.1400.1630.6070.0000.0000.4480.3170.3870.2030.2370.3030.5120.3490.8690.2931.0000.0000.000
554, stonline.sk: count(campaign)0.2150.0000.0000.3450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6570.9830.9830.9830.3140.6570.2940.7160.1470.3940.0000.0001.0000.683
554, yahoo.com: count(campaign)0.0000.0000.0000.0000.0000.3920.4340.0000.0000.0000.0000.0000.2150.6570.0000.9830.9830.9830.3140.6570.3830.6590.0000.3940.0000.0000.6831.000

Missing values

2024-10-03T02:50:36.638741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-03T02:50:37.368677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

421, azet.sk: count(campaign)421, centrum.sk: count(campaign)421, icloud.com: count(campaign)421, seznam.cz: count(campaign)421, zoznam.sk: count(campaign)421, (other): count(campaign)451, centrum.sk: count(campaign)451, hotmail.com: count(campaign)451, icloud.com: count(campaign)451, zoznam.sk: count(campaign)451, (other): count(campaign)452, gmail.com: count(campaign)452, (other): count(campaign)499, azet.sk: count(campaign)499, centrum.sk: count(campaign)499, seznam.cz: count(campaign)499, zoznam.sk: count(campaign)499, (other): count(campaign)550, gmail.com: count(campaign)550, hotmail.com: count(campaign)550, icloud.com: count(campaign)550, seznam.cz: count(campaign)550, zoznam.sk: count(campaign)550, (other): count(campaign)552, gmail.com: count(campaign)552, seznam.cz: count(campaign)552, (other): count(campaign)554, azet.sk: count(campaign)554, centrum.sk: count(campaign)554, stonline.sk: count(campaign)554, yahoo.com: count(campaign)554, (other): count(campaign)
620000000110120000012001062704000013
630000000012034120000700000000000000
640000000000000000004000012280200003
651000000011043920000800000000000000
6600000000000100000010000010000000
67000000000000000000400008280100100
6800000000100343300001220000010000001
690000000000000000003000042801039700166
702000002211034320110121000020000260111
710000000000000000012000010280400010
421, azet.sk: count(campaign)421, centrum.sk: count(campaign)421, icloud.com: count(campaign)421, seznam.cz: count(campaign)421, zoznam.sk: count(campaign)421, (other): count(campaign)451, centrum.sk: count(campaign)451, hotmail.com: count(campaign)451, icloud.com: count(campaign)451, zoznam.sk: count(campaign)451, (other): count(campaign)452, gmail.com: count(campaign)452, (other): count(campaign)499, azet.sk: count(campaign)499, centrum.sk: count(campaign)499, seznam.cz: count(campaign)499, zoznam.sk: count(campaign)499, (other): count(campaign)550, gmail.com: count(campaign)550, hotmail.com: count(campaign)550, icloud.com: count(campaign)550, seznam.cz: count(campaign)550, zoznam.sk: count(campaign)550, (other): count(campaign)552, gmail.com: count(campaign)552, seznam.cz: count(campaign)552, (other): count(campaign)554, azet.sk: count(campaign)554, centrum.sk: count(campaign)554, stonline.sk: count(campaign)554, yahoo.com: count(campaign)554, (other): count(campaign)
8300000000902521000014000000271201350015
8444000150008015210000161000012911311001
85330019629080106020140047000007201056400332
8600000000000000000000000000000000
876000000000100000002000083002011004
880000000080153200001800000000000000
8900000000001000000000000133101020030
900000000080252200001600000000000000
9100000000000010000000000000000000
922400016200801493000216000006271000102

Duplicate rows

Most frequently occurring

421, azet.sk: count(campaign)421, centrum.sk: count(campaign)421, icloud.com: count(campaign)421, seznam.cz: count(campaign)421, zoznam.sk: count(campaign)421, (other): count(campaign)451, centrum.sk: count(campaign)451, hotmail.com: count(campaign)451, icloud.com: count(campaign)451, zoznam.sk: count(campaign)451, (other): count(campaign)452, gmail.com: count(campaign)452, (other): count(campaign)499, azet.sk: count(campaign)499, centrum.sk: count(campaign)499, seznam.cz: count(campaign)499, zoznam.sk: count(campaign)499, (other): count(campaign)550, gmail.com: count(campaign)550, hotmail.com: count(campaign)550, icloud.com: count(campaign)550, seznam.cz: count(campaign)550, zoznam.sk: count(campaign)550, (other): count(campaign)552, gmail.com: count(campaign)552, seznam.cz: count(campaign)552, (other): count(campaign)554, azet.sk: count(campaign)554, centrum.sk: count(campaign)554, stonline.sk: count(campaign)554, yahoo.com: count(campaign)554, (other): count(campaign)# duplicates
0000000000000000000000000000000003